Abstract
Quantifying errors and uncertainties associated with satellite precipitation products (SPPs) is fundamental to guarantee their correct use in several applications, including hydrological predictions, climate studies, and water resource management. Numerous factors affect the accuracy and precision of these products, including the sensor frequencies and channels, the type of precipitation, the heterogeneity of precipitation within the sensor footprint, as well as the choice of algorithm that transfers the sensor retrieval information to a precipitation rate. This chapter analyses these sources and summarizes the most common methods to estimate, quantify, and model errors and uncertainties associated with SPPs.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsReferences
Adler, R. F., Wang, J.-J., Gu, G., & Huffman, G. J. (2009). A ten-year tropical rainfall climatology based on a composite of TRMM products. Journal of the Meteorological Society of Japan, 87, 281–293. https://doi.org/10.2151/jmsj.87A.281.
AghaKouchak, A., Behrangi, A., Sorooshian, S., Hsu, K., & Amitai, E. (2011). Evaluation of satellite-retrieved extreme precipitation rates across the Central United States. Journal of Geophysical Research: Atmospheres, 116(D2). https://doi.org/10.1029/2010JD014741.
AghaKouchak, A., Mehran, A., Norouzi, H., & Behrangi, A. (2012). Systematic and random error components in satellite precipitation data sets. Geophysical Research Letters, 39, L09406. https://doi.org/10.1029/2012GL051592.
Alemohammad, S. H., McColl, K. A., Konings, A. G., Entekhabi, D., & Stoffelen, A. (2015). Characterization of precipitation product errors across the United States using multiplicative triple collocation. Hydrology and Earth System Sciences, 19, 3489–3503. https://doi.org/10.5194/hess-19-3489-2015.
Ali, A., Amani, A., Diedhiou, A., & Lebel, T. (2005). Rainfall estimation in the Sahel. Part II: Evaluation of rain gauge networks in the CILSS countries and objective intercomparison of rainfall products. Journal of Applied Meteorology, 44, 1707–1722. https://doi.org/10.1175/JAM2305.1.
Anagnostou, E. N., Maggioni, V., Nikolopoulos, E. I., Meskele, T., Hossain, F., & Papadopoulos, A. (2010). Benchmarking high resolution global satellite rainfall products to radar and rain-gauge rainfall estimates. IEEE Transactions on Geoscience and Remote Sensing, 48, 1667–1683. https://doi.org/10.1109/TGRS.2009.2034736.
Behrangi, A., & Wen, Y. (2017). On the spatial and temporal sampling errors of remotely sensed precipitation products. Remote Sensing, 9, 1127. https://doi.org/10.3390/rs9111127.
Bell, T. L., Abdullah, A., Martin, R. L., & North, G. R. (1990). Sampling errors for satellite-derived tropical rainfall: Monte Carlo study using a space-time stochastic model. Journal of Geophysical Research, 95, 2195–2205. https://doi.org/10.1029/JD095iD03p02195.
Bellerby, T., & Sun, J. (2005). Probabilistic and ensemble repre- sentations of the uncertainty in an IR/microwave satellite precipitation product. Journal of Hydrometeorology, 6, 1032–1044. https://doi.org/10.1175/JHM454.1.
Brocca, L., Ciabatta, L., Massari, C., Moramarco, T., Hahn, S., Hasenauer, S., Kidd, R., Dorigo, W., Wagner, W., & Levizzani, V. (2014). Soil as a natural rain gauge: Estimating global rainfall from satellite soil moisture data. Journal of Geophysical Research, 119, 5128–5141. https://doi.org/10.1002/2014JD021489.
Bytheway, J. L., & Kummerow, C. D. (2013). Inferring the uncertainty of satellite precipitation estimates in data-sparse regions over land. Journal of Geophysical Research, 118, 9524–9533. https://doi.org/10.1002/jgrd.50607.
Ciabatta, L., Marra, A. C., Panegrossi, G., Casella, D., Sanò, P., Dietrich, S., Massari, C., & Brocca, L. (2017). Daily precipitation estimation through different microwave sensors: Verification study over Italy. Journal of Hydrology, 545, 436–450. https://doi.org/10.1016/j.jhydrol.2016.12.057.
Crow, W. T., & van den Berg, M. J. (2010). An improved approach for estimating observation and model error parameters in soil moisture data assimilation. Water Resources Research, 46, W12519. https://doi.org/10.1029/2010WR009402.
Dinku, T., & Anagnostou, E. N. (2005). Regional differences in overland rainfall estimation from PR-calibrated TMI algorithm. Journal of Applied Meteorology, 44, 189–205. https://doi.org/10.1175/JAM2186.1.
Dinku, T., Ceccato, P., Grover-Kopec, E., Lemma, M., Connor, S. J., & Ropelewski, C. F. (2007). Validation of satellite rainfall products over East Africa’s complex topography. International Journal of Remote Sensing, 28, 1503–1526. https://doi.org/10.1080/01431160600954688.
Dorigo, W. A., Scipal, K., Parinussa, R. M., Liu, Y. Y., Wagner, W., de Jeu, R. A. M., & Naeimi, V. (2010). Error characterisation of global active and passive microwave soil moisture datasets. Hydrology and Earth System Sciences, 14, 2605–2616. https://doi.org/10.5194/hess-14-2605-2010.
Ebert, E. E. (2007). Methods for verifying satellite precipitation estimates. In V. Levizzani, P. Bauer, & F. J. Turk (Eds.), Measuring precipitation from space (Advances global change research) (Vol. 28, pp. 345–356). Dordrecht: Springer. ISBN: 978-1-4020-5835-6.
Ebert, E. E., Janowiak, J. E., & Kidd, C. (2007). Comparison of near-real-time precipitation estimates from satellite observa- tions and numerical models. Bulletin of the American Meteorological Society, 88, 47–64. https://doi.org/10.1175/BAMS-88-1-47.
Falck, A. S., Maggioni, V., Tomasella, J., Vila, D. A., & Diniz, F. L. R. (2015). Propagation of satellite precipitation uncertainties through a distributed hydrologic model: A case study in the Tocantins-Araguaia basin in Brazil. Journal of Hydrology, 527, 943–957. https://doi.org/10.1016/j.jhydrol.2015.05.042.
Gebregiorgis, A. S., & Hossain, F. (2013). Understanding the dependence of satellite rainfall uncertainty on topography and climate for hydrologic model simulation. IEEE Transactions on Geoscience and Remote Sensing, 51, 704–718. https://doi.org/10.1109/TGRS.2012.2196282.
Gebremichael, M., & Krajewski, W. F. (2004). Characterization of the temporal sampling error in space-time-averaged rainfall estimates from satellites. Journal of Geophysical Research, 109, D11110. https://doi.org/10.1029/2004JD004509.
Gebremichael, M., & Krajewski, W. F. (2005). Modeling distribution of temporal sampling errors in area-time-averaged rainfall estimates. Atmospheric Research, 73, 243–259. https://doi.org/10.1016/j.atmosres.2004.11.004.
Gebremichael, M., Liao, G.-Y., & Yan, J. (2011). Nonparametric error model for a high resolution satellite rainfall product. Water Resources Research, 47, W07504. https://doi.org/10.1029/2010WR009667.
Gottschalck, J., Meng, J., Rodell, M., & Houser, P. (2005). Analysis of multiple precipitation products and preliminary assessment of their impact on global land data assimilation system land surface states. Journal of Hydrometeorology, 6, 573–598. https://doi.org/10.1175/JHM437.1.
Herold, N., Alexander, L. V., Donat, M. G., Contractor, S., & Becker, A. (2015). How much does it rain over land? Geophysical Research Letters, 43, 341–348. https://doi.org/10.1002/2015GL066615.
Hirpa, F. A., Gebremichael, M., & Hopson, T. (2010). Evaluation of high-resolution satellite precipitation products over very complex terrain in Ethiopia. Journal of Applied Meteorology and Climatology, 49, 1044–1051. https://doi.org/10.1175/2009JAMC2298.1.
Hong, Y., Hsu, K.-L., Moradkhani, H., & Sorooshian, S. (2006). Uncertainty quantification of satellite precipitation estimation and Monte Carlo assessment of the error propagation into hydrologic response. Water Resources Research, 42, W08421. https://doi.org/10.1029/2005WR004398.
Hossain, F., & Anagnostou, E. N. (2004). Assessment of current passive-microwave- and infrared-based satellite rainfall remote sensing for flood prediction. Journal of Geophysical Research, 109, D07102. https://doi.org/10.1029/2003JD003986.
Hossain, F., Anagnostou, E. N., Dinku, T., & Borga, M. (2004). Hydrological model sensitivity to parameter and radar rainfall estimation uncertainty. Hydrological Processes, 18, 3277–3291. https://doi.org/10.1002/hyp.5659.
Huffman, G. J. (1997). Estimates of root-mean-square random error for finite samples of estimated precipitation. Journal of Applied Meteorology, 36, 1191–1201. https://doi.org/10.1175/1520-0450(1997)036,1191:EORMSR.2.0.CO;2.
Huffman, G. J., Adler, R. F., Arkin, P., Chang, A., Ferraro, R., Gruber, A., Janowiak, J., McNab, A., Rudolf, B., & Schneider, U. (1997). The global precipitation climatology project (GPCP) combined precipitation dataset. Bulletin of the American Meteorological Society, 78(1), 5–20. https://doi.org/10.1175/1520-0477(1997)078<0005:TGPCPG>2.0.CO;2.
Huffman, and Coauthors. (2007). The TRMM multisatellite Precipita- tion analysis (TMPA): Quasi-global, multiyear, combined- sensor precipitation estimates at fine scales. Journal of Hydrometeorology, 8, 38–55. https://doi.org/10.1175/JHM560.1.
Jolliffe, I. T., & Stephenson, D. B. (2012). Forecast verification: A practitioner’s guide in atmospheric science (2nd ed.). Somerset. 274 pp: Wiley. https://doi.org/10.1002/9781119960003.
Kidd, C., Becker, A., Huffman, G. J., Muller, C. L., Joe, P., Skofronick-Jackson, G., & Kirschbaum, D. B. (2017). So, how much of the earth’s surface is covered by rain gauges? Bulletin of the American Meteorological Society, 98, 69–78. https://doi.org/10.1175/BAMS-D-14-00283.1.
Klepp, C., Michel, S., Protat, A., Burdanowitz, J., Albern, N., Kähnert, M., Dahl, A., Louf, V., Bakan, S., & Buehler, S. A. (2018). OceanRAIN, a new in-situ shipboard global ocean surface-reference dataset of all water cycle components. Scientific Data, 5, 180122. https://doi.org/10.1038/sdata.2018.122.
Kummerow, C. D., Berg, W., Thomas-Stahle, J., & Masunaga, H. (2006). Quantifying global uncertainties in a simple microwave rainfall algorithm. Journal of Atmospheric and Oceanic Technology, 23, 23–37. https://doi.org/10.1175/JTECH1827.1.
Maggioni, V., & Massari, C. (2018). On the performance of satellite precipitation products in riverine flood modeling: A review. Journal of Hydrology, 558, 214–224. https://doi.org/10.1016/J.JHYDROL.2018.01.039.
Maggioni, V., Vergara, H. J., Anagnostou, E. N., Gourley, J. J., Hong, Y., & Stampoulis, D. (2013). Investigating the applicability of error correction ensembles of satellite rainfall products in river flow simulations. Journal of Hydrometeorology, 14, 1194–1211. https://doi.org/10.1175/JHM-D-12-074.1.
Maggioni, V., Sapiano, M. R. P., Adler, R. F., Tian, Y., & Huffman, G. J. (2014). An error model for uncertainty quantification in high-time-resolution precipitation products. Journal of Hydrometeorology, 15, 1274–1292. https://doi.org/10.1175/JHM-D-13-0112.1.
Maggioni, V., Meyers, P. C., & Robinson, M. D. (2016a). A review of merged high-resolution satellite precipitation product accuracy during the tropical rainfall measuring mission (TRMM) era. Journal of Hydrometeorology, 17, 1101–1117. https://doi.org/10.1175/JHM-D-15-0190.1.
Maggioni, V., Sapiano, M. R. P., & Adler, R. F. (2016b). Estimating uncertainties in high-resolution satellite precipitation products: Systematic or random error? Journal of Hydrometeorology, 17, 1119–1129. https://doi.org/10.1175/JHM-D-15-0094.1.
Massari, C., Crow, W., & Brocca, L. (2017). An assessment of the performance of global rainfall estimates without ground-based observations. Hydrology and Earth System Sciences, 21, 4347–4361. https://doi.org/10.5194/hess-21-4347-2017.
Moazami, S., Golian, S., Kavianpour, M. R., & Hong, Y. (2013). Comparison of PERSIANN and V7 TRMM multi-satellite precipitation analysis (TMPA) products with rain gauge data over Iran. International Journal of Remote Sensing, 34, 8156–8171. https://doi.org/10.1080/01431161.2013.833360.
Nijssen, B., & Lettenmaier, D. P. (2003). Effect of precipitation sampling error on simulated hydrological fluxes and states: Anticipating the global precipitation measurement satellites. Journal of Geophysical Research, 109, D02103. https://doi.org/10.1029/2003JD003497.
Nikolopoulos, E. I., Destro, E., Maggioni, V., Marra, F., & Borga, M. (2017). Satellite rainfall estimates for debris flow prediction: An evaluation based on rainfall accumulation–duration thresholds. Journal of Hydrometeorology, 18, 2207–2214. https://doi.org/10.1175/JHM-D-17-0052.1.
Oliveira, R., Maggioni, V., Vila, D., & Morales, C. (2016). Characteristics and diurnal cycle of GPM rainfall estimates over the Central Amazon region. Remote Sensing, 8, 544. https://doi.org/10.3390/rs8070544.
Oliveira, R., Maggioni, V., Vila, D., & Porcacchia, L. (2018). Using satellite error modeling to improve GPM-level 3 rainfall estimates over the Central Amazon region. Remote Sensing, 10, 336. https://doi.org/10.3390/rs10020336.
Roca, R., Chambon, P., Jobard, I., Kirstetter, P.-E., Gosset, M., & Bergès, J. C. (2010). Comparing satellite and surface rainfall products over West Africa at meteorologically relevant scales during the AMMA campaign using error estimates. Journal of Applied Meteorology and Climatology, 49, 715–731. https://doi.org/10.1175/2009JAMC2318.1.
Roebber, P. J. (2009). Visualizing multiple measures of forecast quality. Weather and Forecasting, 24, 601–608. https://doi.org/10.1175/2008WAF2222159.1.
Roebeling, R. A., Wolters, E. L. A., Meirink, J. F., & Leijnse, H. (2012). Triple collocation of summer precipitation retrievals from SEVIRI over Europe with gridded rain gauge and weather radar data. Journal of Hydrometeorology, 13, 1552–1566. https://doi.org/10.1175/JHM-D-11-089.1.
Schaake, J. C., Hamill, T. M., Buizza, R., & Clark, M. (2007). HEPEX: The hydrological ensemble prediction experiment. Bulletin of the American Meteorological Society, 88, 1541–1548. https://doi.org/10.1175/BAMS-88-10-1541.
Serrat-Capdevila, A., Valdes, J. B., & Stakhiv, E. Z. (2014). Water management applications for satellite precipitation products: Synthesis and recommendations. Journal of the American Water Resources Association, 50, 509–525. https://doi.org/10.1111/jawr.12140.
Stampoulis, D., Anagnostou, E. N., & Nikolopoulos, E. I. (2013). Assessment of high-resolution satellite-based rainfall estimates over the Mediterranean during heavy precipitation events. Journal of Hydrometeorology, 14, 1500–1514. https://doi.org/10.1175/JHM-D-12-0167.1.
Steiner, M., Bell, T. L., Zhang, Y., & Wood, E. F. (2003). Comparison of two methods for estimating the sampling-related uncertainty of satellite rainfall averages based on a large radar dataset. Journal of Climate, 16(22), 3759–3778. https://doi.org/10.1175/1520-0442(2003)016<3759:COTMFE>2.0.CO;2.
Stephens, G. L., & Kummerow, C. D. (2007). The remote sensing of clouds and precipitation from space: A review. Journal of the Atmospheric Sciences, 64, 3742–3765. https://doi.org/10.1175/2006JAS2375.1.
Stoffelen, A. (1998). Toward the true near-surface wind speed: Error modeling and calibration using triple collocation. Journal of Geophysical Research, 103, 7755–7766. https://doi.org/10.1029/97JC03180.
Tang, L., & Hossain, F. (2012). Investigating the similarity of satellite rainfall error metrics as a function of Köppen climate classification. Atmospheric Research, 104–105, 182–192. https://doi.org/10.1016/j.atmosres.2011.10.006.
Taylor, K. E. (2001). Summarizing multiple aspects of model performance in a single diagram. Journal of Geophysical Research, 106, 7183–7192. https://doi.org/10.1029/2000JD900719.
Tian, Y., & Peters-Lidard, C. D. (2010). A global map of uncertainties in satellite-based precipitation measurements. Geophysical Research Letters, 37, L24407. https://doi.org/10.1029/2010GL046008.
Tian, Y., Peters-Lidard, C. D., Choudhury, B. J., & Garcia, M. (2007). Multitemporal analysis of TRMM-based satellite precipitation products for land data assimilation applications. Journal of Hydrometeorology, 8, 1165–1183. https://doi.org/10.1175/2007JHM859.1.
Tian, Y., Huffman, G. J., Adler, R. F., Tang, L., Sapiano, M., Maggioni, V., & Wu, H. (2013). Modeling errors in daily precipitation measurements: Additive or multiplicative? Geophysical Research Letters, 40, 2060–2065. https://doi.org/10.1002/grl.50320.
Todini, E. (2001). A Bayesian technique for conditioning radar precipitation estimates to rain-gauge measurements. Hydrology and Earth System Sciences, 5, 187–199. https://doi.org/10.5194/hess-5-187-2001.
Villarini, G., & Krajewski, W. F. (2007). Evaluation of the research version TMPA three-hourly three-hourly 0.25° × 0.25° rainfall estimates over Oklahoma. Geophysical Research Letters, 34, L05402. https://doi.org/10.1029/2006GL029147.
Villarini, G., Mandapaka, P. V., Krajewski, W. F., & Moore, R. J. (2008). Rainfall and sampling uncertainties: A rain gauge perspective. Journal of Geophysical Research, 113, W07504. https://doi.org/10.1029/2007JD009214.
Xie, P., & Arkin, P. A. (1995). An intercomparison of gauge observations and satellite estimates of monthly precipitation. Journal of Applied Meteorology, 34, 1143–1160. https://doi.org/10.1175/1520-0450(1995)034<1143:AIOGOA>2.0.CO;2.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2020 Springer Nature Switzerland AG
About this chapter
Cite this chapter
Massari, C., Maggioni, V. (2020). Error and Uncertainty Characterization. In: Levizzani, V., Kidd, C., Kirschbaum, D., Kummerow, C., Nakamura, K., Turk, F. (eds) Satellite Precipitation Measurement. Advances in Global Change Research, vol 69. Springer, Cham. https://doi.org/10.1007/978-3-030-35798-6_4
Download citation
DOI: https://doi.org/10.1007/978-3-030-35798-6_4
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-35797-9
Online ISBN: 978-3-030-35798-6
eBook Packages: Earth and Environmental ScienceEarth and Environmental Science (R0)